People analytics, the application of scientific and statistical methods to behavioral data, traces its origins to Frederick Winslow Taylor's classic The Principles of Scientific Management in 1911, which sought to apply engineering methods to the management of people. But it wasn't until a century later -- after advances in computer power, statistical methods, and especially artificial intelligence (AI) -- that the field truly exploded in power, depth, and widespread application, especially, but not only, in Human Resources (HR) management. By automating the collection and analysis of large datasets, AI and other analytics tools offer the promise of improving every phase of the HR pipeline, from recruitment and compensation to promotion, training, and evaluation. Now, algorithms are being used to help managers measure productivity and make important decisions in hiring, compensation, promotion, and training opportunities -- all of which may be life-changing for employees. Firms are using this technology to identify and close pay gaps across gender, race, or other important demographic categories.
In today’s digital era, artificial intelligence (AI) and machine learning (ML) are everywhere – from facial recognition to algorithms pandemic outbreak mitigation and healthcare. What AI does is automate the judgments as —yes, no; right, wrong. But are these technologies, that can mirror human intelligence, built in consensus with human ethics? Can we create new..
In today's digital era, artificial intelligence (AI) and machine learning (ML) are everywhere - from facial recognition to algorithms pandemic outbreak mitigation and healthcare. What AI does is automate the judgments as --yes, no; right, wrong. Today, AI is becoming ubiquitous, in and out of the workplace. With artificial intelligence (AI) becoming more powerful, the questions that surround AI ethics are becoming more relevant. But can technology be controlled to avoid adverse outcomes?
This story is from The Hill's Changing America publication. The job of a college admissions officer is not an easy one. For any competitive higher learning institution, the admissions process used to hand-pick each incoming student has also come under increasing scrutiny in recent years. To ensure the ongoing success of an institution, admissions officers are tasked with the nearly impossible task of efficiently evaluating thousands of applications each school year, with the expectation that their choices will reflect the institution's standards, grow diversity and inspire enough students to enroll. The process is a balancing act, and one that is expected to proceed without gender-based or racial bias.
Decision-making has mostly revolved around learning from mistakes and making gradual, steady improvements. For several centuries, evolutionary experience has served humans well when it comes to decision-making. So, it is safe to say that most decisions human beings make are based on trial and error. Additionally, humans rely heavily on data to make key decisions. Larger the amount of high-integrity data available, the more balanced and rational their decisions will be.